Search results for: stochastic preferences

Authors: Ana Rita Conde, Pilar Mota, Tânia Botelho, Carlos Rodrigues, Osvaldo Silva, Áurea Sousa, Suzana Caldeira, Isabel Rego, Jéssica Pacheco

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Families with children with ASD are interested in traveling but end up not traveling due to the obstacles they face and not finding inclusive traveling offers. This study will identify the needs of families with children with ASD, to develop the products targeted to their tourist needs. 137 families from different countries answered a questionnaire about their travel experiences, needs and preferences. Based on the results, guidelines are presented for the development of products specially aimed for this market niche.

Keywords: inclusive tourism, sustainability, autism spectrum disorder, children, families

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Authors: Kamariah Abu Bakar, Jennifer Way

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This study investigated how young children (six years old) constructed and used representations in mathematics classroom; particularly in problem solving. The purpose of this study is to explore the ways children used representations in solving addition problems and to determine whether their representations can play a supportive role in understanding the problem situation and solving them correctly. Data collection includes observations, children’s artifact, photographs and conversation with children during task completion. The results revealed that children were able to construct and use various representations in solving problems. However, they have certain preferences in generating representations to support their problem solving.

Keywords: young children, representations, addition, problem solving

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Authors: Delia Gabriela Moisa, Demos Parapanos, Tim Heap

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The tourism sector is mainly comprised of small and medium-sized tourism enterprises (SMEs), representing approximately 80% of global businesses in this field. These entities require focused attention and support to address challenges, ensuring their competitiveness and relevance in a dynamic industry characterized by continuously changing customer preferences. To address these challenges, it becomes imperative to consider not only socio-demographic factors but also delve into the intricate interplay of psychological elements influencing consumer behavior. This study investigates the impact of personality traits and travel motivations on visitor activities in Cumbria, United Kingdom, an iconic region marked by UNESCO World Heritage Sites, including The Lake District National Park and Hadrian's Wall. With a £4.1 billion tourism industry primarily driven by SMEs, Cumbria serves as an ideal setting for examining the relationship between tourist psychology and activities. Employing the Big Five personality model and the Travel Career Pattern motivation theory, this study aims to explain the relationship between psychological factors and tourist activities. The study further explores SME perspectives on personality-based market segmentation, providing strategic insights into addressing evolving tourist preferences.This pioneering mixed-methods study integrates quantitative data from 330 visitor surveys, subsequently complemented by qualitative insights from tourism SME representatives. The findings unveil that socio-demographic factors do not exhibit statistically significant variations in the activities pursued by visitors in Cumbria. However, significant correlations emerge between personality traits and motivations with preferred visitor activities. Open-minded tourists gravitate towards events and cultural activities, while Conscientious individuals favor cultural pursuits. Extraverted tourists lean towards adventurous, recreational, and wellness activities, while Agreeable personalities opt for lake cruises. Interestingly, a contrasting trend emerges as Extraversion increases, leading to a decrease in interest in cultural activities. Similarly, heightened Agreeableness corresponds to a decrease in interest in adventurous activities. Furthermore, travel motivations, including nostalgia and building relationships, drive event participation, while self-improvement and novelty-seeking lead to adventurous activities. Additionally, qualitative insights from tourism SME representatives underscore the value of targeted messaging aligned with visitor personalities for enhancing loyalty and experiences. This study contributes significantly to scholarship through its novel framework, integrating tourist psychology with activities and industry perspectives. The proposed conceptual model holds substantial practical implications for SMEs to formulate personalized offerings, optimize marketing, and strategically allocate resources tailored to tourist personalities. While the focus is on Cumbria, the methodology's universal applicability offers valuable insights for destinations globally seeking a competitive advantage. Future research addressing scale reliability and geographic specificity limitations can further advance knowledge on this critical relationship between visitor psychology, individual preferences, and industry imperatives. Moreover, by extending the investigation to other districts, future studies could draw comparisons and contrasts in the results, providing a more nuanced understanding of the factors influencing visitor psychology and preferences.

Keywords: personality trait, SME, tourist behaviour, tourist motivation, visitor activity

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Authors: Hacene Benkhoula, Mohamed Badreddine Benabdella, Hamid Bouzeboudja, Abderrahmane Asraoui

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The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given.

Keywords: wind power, Gaussien process, modelling, forecasting

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Authors: Ipek Kivanc, Demet Ozgur-Unluakin

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Over the past years, technological innovations and advancements have played an important role in the industrial world. Due to technological improvements, the degree of complexity of the systems has increased. Hence, all systems are getting more uncertain that emerges from increased complexity, resulting in more cost. It is challenging to cope with this situation. So, implementing efficient planning of maintenance activities in such systems are getting more essential. Partially Observable Markov Decision Processes (POMDPs) are powerful tools for stochastic sequential decision problems under uncertainty. Although maintenance optimization in a dynamic environment can be modeled as such a sequential decision problem, POMDPs are not widely used for tackling maintenance problems. However, they can be well-suited frameworks for obtaining optimal maintenance policies. In the classical representation of the POMDP framework, the system is denoted by a single node which has multiple states. The main drawback of this classical approach is that the state space grows exponentially with the number of state variables. On the other side, factored representation of POMDPs enables to simplify the complexity of the states by taking advantage of the factored structure already available in the nature of the problem. The main idea of factored POMDPs is that they can be compactly modeled through dynamic Bayesian networks (DBNs), which are graphical representations for stochastic processes, by exploiting the structure of this representation. This study aims to demonstrate how maintenance planning of dynamic systems can be modeled with factored POMDPs. An empirical maintenance planning problem of a dynamic system consisting of four partially observable components deteriorating in time is designed. To solve the empirical model, we resort to Symbolic Perseus solver which is one of the state-of-the-art factored POMDP solvers enabling approximate solutions. We generate some more predefined policies based on corrective or proactive maintenance strategies. We execute the policies on the empirical problem for many replications and compare their performances under various scenarios. The results show that the computed policies from the POMDP model are superior to the others. Acknowledgment: This work is supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under grant no: 117M587.

Keywords: factored representation, maintenance, multi-component system, partially observable Markov decision processes

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Authors: Jeffrey Tzu-Hao Tsai, Yi-Shan Wong

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This article proposes a cohort-age-period (CAP) model to characterize multi-population mortality processes using cohort, age, and period variables. Distinct from the factor-based Lee-Carter-type decomposition mortality model, this approach is empirically based and includes the age, period, and cohort variables into the equation system. The model not only provides a fruitful intuition for explaining multivariate mortality change rates but also has a better performance in forecasting future patterns. Using the US and the UK mortality data and performing ten-year out-of-sample tests, our approach shows smaller mean square errors in both countries compared to the models in the literature.

Keywords: longevity risk, stochastic mortality model, multivariate mortality rate, risk management

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Authors: Alexander E. Hramov, Vadim V. Grubov, Alexey A. Koronovskii, Maria K. Kurovskaуa, Anastasija E. Runnova

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The perception of visual signals in the brain was among the first issues discussed in terms of multistability which has been introduced to provide mechanisms for information processing in biological neural systems. In this work the influence of the cognitive noise on the visual perception of multistable pictures has been investigated. The study includes an experiment with the bistable Necker cube illusion and the theoretical background explaining the obtained experimental results. In our experiments Necker cubes with different wireframe contrast were demonstrated repeatedly to different people and the probability of the choice of one of the cubes projection was calculated for each picture. The Necker cube was placed at the middle of a computer screen as black lines on a white background. The contrast of the three middle lines centered in the left middle corner was used as one of the control parameter. Between two successive demonstrations of Necker cubes another picture was shown to distract attention and to make a perception of next Necker cube more independent from the previous one. Eleven subjects, male and female, of the ages 20 through 45 were studied. The choice of the Necker cube projection was detected with the Electroencephalograph-recorder Encephalan-EEGR-19/26, Medicom MTD. To treat the experimental results we carried out theoretical consideration using the simplest double-well potential model with the presence of noise that led to the Fokker-Planck equation for the probability density of the stochastic process. At the first time an analytical solution for the probability of the selection of one of the Necker cube projection for different values of wireframe contrast have been obtained. Furthermore, having used the results of the experimental measurements with the help of the method of least squares we have calculated the value of the parameter corresponding to the cognitive noise of the person being studied. The range of cognitive noise parameter values for studied subjects turned to be [0.08; 0.55]. It should be noted, that experimental results have a good reproducibility, the same person being studied repeatedly another day produces very similar data with very close levels of cognitive noise. We found an excellent agreement between analytically deduced probability and the results obtained in the experiment. A good qualitative agreement between theoretical and experimental results indicates that even such a simple model allows simulating brain cognitive dynamics and estimating important cognitive characteristic of the brain, such as brain noise.

Keywords: bistability, brain, noise, perception, stochastic processes

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Authors: Y. de Smet, J. Hubinont

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PROMETHEE and GAIA are well-known Multiple Criteria Decision Aid methods. Given an evaluation table and preference parameters they allow to rank the alternatives, to visualize the problem, to perform sensitivity and robustness analysis, etc. Unfortunately, it is often hard for the Decision Maker (DM) to estimate the precise values of these parameters. Therefore an alternative option is to give ranges of potential values in order to apply Stochastic Multicriteria Acceptability Analysis. This has been recently studied in the context of the SMAA-PROMETHEE method. The aim of this contribution is to propose an SMAA extension of GAIA. We show how this tool can be useful and provide complementary information to SMAA-PROMETHEE. This is illustrated on a pedagogical example.

Keywords: multiple criteria decision making, PROMETHEE, GAIA, SMAA

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Authors: Marianna Bolla

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The aim of this paper is to compare spectral, discrepancy, and degree properties of expanding graph sequences. As we can prove equivalences and implications between them and the definition of the generalized (multiclass) quasirandomness of Lovasz–Sos (2008), they can be regarded as generalized quasirandom properties akin to the equivalent quasirandom properties of the seminal Chung-Graham-Wilson paper (1989) in the one-class scenario. Since these properties are valid for deterministic graph sequences, irrespective of stochastic models, the partial implications also justify for low-dimensional embedding of large-scale graphs and for discrepancy minimizing spectral clustering.

Keywords: generalized random graphs, multiway discrepancy, normalized modularity spectra, spectral clustering

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Authors: Rhaetian Bern D. Azaula

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The Philippines' indigenous population, estimated to be 10%-20%, is protected by the Indigenous Peoples Rights Act (IPRA), passed in 1997. However, due to their isolation and limited access to basic services such as health education or needs for health assistance, the law's implementation remains a challenge. As traditional medicine continues to play a significant role in society as the prevention and treatment of some illnesses, it is still customary and widely used to use plants in both traditional and modern ways; however, commercially prepared drugs are progressively advanced as time goes by. Therefore, the purpose of this quantitative study is to investigate the indigenous community at Barangay Magsikap General Nakar, Quezon, and analyze the factors that affect the respondent’s preferences in an indigenous community and reasons for patronizing traditional and commercially prepared medicines and proposes updated health education strategies and instructional materials. Slovin's formula was utilized to reduce the total population representation, followed by stratified sampling for proportional allocation of respondents. The study selects respondents (1) from an Indigenous Community in Barangay Magsikap, General Nakar, Quezon, (2) aged 60 and above, and (3) who are willing to participate. The researcher utilized a checklist-based questionnaire with a Tagalog version, and a Likert Scale was utilized to assess the respondent's choices on selected items. The researcher obtained informed consent from the indigenous community's regional and local office, the chieftain of the tribe, and the respondents, ensuring confidentiality in the collection and retrieval of data. The study revealed that respondents aged 60-69, males with no formal education, are unemployed and have no income source. They prefer traditional medicines due to their affordability, availability, and cultural practices but lack safe preparation, dosages, and contraindications of used medicines. Commercially prepared medications are acknowledged, but respondents are not fully aware of proper administration instructions and dosage labels. Recommendations include disseminating approved herbal medicines and ensuring proper preparation, indications, and contraindications.

Keywords: traditional medicine, commercially prepared medicine, indigenous community, elderly adult

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Authors: Poonam Goswami

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Background: The diagnosis of a serious illness like cancer can have an impact on a patient’s emotional well-being and may result in psychological symptoms, anxiety, depression, and loss of control. Advance care planning discussions ensure patients’ values and goals of care, including patients’ freedom to choose their place of death, are respected. Unfortunately, these discussions are often delayed and are not initiated early in patients’ cancer trajectory. As a result, patients’ wishes often remains unknown until the last phase of their life. Evidence suggests that many patients inappropriately receive aggressive treatment near the end of life, which does lead to higher resource utilization, decreased quality of life, and increased cost. Additionally, the novel coronavirus disease 2019 (COVID-19) pandemic challenged the health care systems worldwide and raised important ethical issues, especially regarding the potential need for rationing health care in the context of scarce resources and crisis capacity. The importance of goal concordant care is now even substantially important and is heightened in the context of this pandemic. Problem: Although there is growing evidence on the effects of the ACP on the completion of advanced directives, improved patient and family concordance for preferences for medical care, and receipt of care, there is still a lack of standardized ACP conversation strategies for patients with cancer. Methods: The Key concepts of ACP include (1) assessing patient and family readiness, (2) identifying a surrogate decision maker ( medical power of attorney), (3) exploring patient and family understanding of the disease and treatment options,(4) discussing the values and goals of care, and options for end-of-life care, (5) documenting patient preferences in the medical record, and (6) revisiting the discussions at every change in the treatment plan and /or change in clinical status, including at every hospitalization. Conclusion/Implication for practice: Advance Care Planning (ACP) and end-of-life (EOL) discussions are important for patients, families, and health care providers. Adopting the verbal and nonverbal communication strategies can help overcome the barriers to effective communication on these difficult discussions. ACP with goals of care discussions should not be delayed until the patient is hospitalized.

Keywords: advance care planning, end of life, cancer, global, pandemic

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Authors: Christine K. Ormsbee, Jeremy S. Robinson

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Teacher quality is important for United States universities. It impacts student achievement, program and degree progress, and even retention. While course instructors are still the primary designers and deliverers of instruction in U.S. higher education classrooms, students have become better and more vocal consumers of instruction. They are capable of identifying what instructors do that facilitates their learning or, conversely, what instructors do that makes learning more difficult. Instructors can use students as resources as they design and implement their courses. Students have become more aware of their own learning preferences and processes and can articulate those. While it is not necessarily possible or likely that an instructor can address the widely varying differences in learning preferences represented by a large class of students, it is possible for them to employ general instructional supports that help students understand clearly the instructor's study expectations, identify critical content, efficiently commit content to memory, and develop new skills. Those learning supports include reading guides, test study guides, and other instructor-developed tasks that organize learning for students, hold them accountable for the content, and prepare them to use that material in simulated and real situations. When U.S. university teaching and learning support staff work with instructors to help them identify areas of their teaching to improve, a key part of that assistance includes talking to the instructor member's students. Students are asked to explain what the instructor does that helps them learn, what the instructor does that impedes their learning, and what they wish the instructor would do. Not surprisingly, students are very specific in what they see as helpful learning supports for them. Moreover, they also identify impediments to their success, viewing those as the instructor creating unnecessary barriers to learning. A qualitative survey was developed to provide undergraduate students the opportunity to identify instructor behaviors and/or practices that they thought helped students learn and those behaviors and practices that were perceived as hindrances to student success. That information is used to help instructors implement more student-focused learning supports that facilitate student achievement. In this session, data shared from the survey will focus on supportive instructor behaviors identified by undergraduate students in an institution located in the southwest United States and those behaviors that students perceive as creating unnecessary barriers to their academic success.

Keywords: effective teaching, pedagogy, student engagement, instructional design

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Authors: Tet H. Yeap

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An analog restricted Hopfield Network is presented in this paper. It consists of two layers of nodes, visible and hidden nodes, connected by directional weighted paths forming a bipartite graph with no intralayer connection. An energy or Lyapunov function was derived to show that the proposed network will converge to stable states. By introducing hidden nodes, the proposed network can be trained to store patterns and has increased memory capacity. Training to be an associative memory, simulation results show that the associative memory performs better than a classical Hopfield network by being able to perform better memory recall when the input is noisy.

Keywords: restricted Hopfield network, Lyapunov function, simultaneous perturbation stochastic approximation

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Y. Y. Pang, N. A. Ismail

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The map is a powerful and convenient tool in helping us to navigate to different places, but the use of indirect devices often makes its usage cumbersome. This study intends to propose a new map navigation dialogue that uses hand gesture. A set of dialogue was developed from users’ perspective to provide users complete freedom for panning, zooming, rotate, and find direction operations. A participatory design experiment was involved here where one hand gesture and two hand gesture dialogues had been analysed in the forms of hand gestures to develop a set of usable dialogues. The major finding was that users prefer one-hand gesture compared to two-hand gesture in map navigation.

Keywords: hand gesture, map navigation, participatory design, intuitive interaction

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Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

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Authors: Shan Wang, Fahad Alhathal, Daniel Hobson

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User-centered design skills play an important role in crafting a positive and intuitive user experience for software applications. Embracing a user-centric design approach involves understanding the needs, preferences, and behaviors of the end-users throughout the design process. This mindset not only enhances the usability of the software but also fosters a deeper connection between the digital product and its users. This paper encompasses a 6-month knowledge exchange collaboration project between an academic institution and an external industry in 2023, aims to improve the user experience of a digital platform utilized for a knowledge management tool, to understand users' preferences for features, identify sources of frustration, and pinpoint areas for enhancement. This research conducted one of the most effective methods to implement user-centered design through co-design workshops for testing user onboarding experiences that involve the active participation of users in the design process. More specifically, in January 2023, we organized eight workshops with a diverse group of 11 individuals. Throughout these sessions, we accumulated a total of 11 hours of qualitative data in both video and audio formats. Subsequently, we conducted an analysis of user journeys, identifying common issues and potential areas for improvement. This analysis was pivotal in guiding the knowledge management software in prioritizing feature enhancements and design improvements. Employing a user-centered design thinking process, we developed a series of graphic design solutions in collaboration with the software management tool company. These solutions were targeted at refining onboarding user experiences, workplace interfaces, and interactive design. Some of these design solutions were translated into tangible interfaces for the knowledge management tool. By actively involving users in the design process and valuing their input, developers can create products that are not only functional but also resonate with the end-users, ultimately leading to greater success in the competitive software landscape. In conclusion, this paper not only contributes insights into designing onboarding user experiences for software within a co-design approach but also presents key theories on leveraging the user-centered design process in software design to enhance overall user experiences.

Keywords: user experiences, co-design, design process, knowledge management tool, user-centered design

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Authors: Florian Pfeffel, Valentin Nickolai, Christian Louis Kühner

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Digitalization has disrupted the traditional workplace environment by allowing many employees to work from anywhere at any time. This trend of working from home was further accelerated due to the COVID-19 crisis, which forced companies to rethink their workplace models. While in many companies, this shift happened out of pure necessity; many employees were left more satisfied with their job due to the opportunity to work from home. This study focuses on employees’ job satisfaction in the service sector in dependence on the different work models, which are defined as a “work from home” model, the traditional “work in office” model, and a hybrid model. Using structural equation modeling (SEM), these three work models have been analyzed based on 13 influencing factors on job satisfaction that have been further summarized in the three groups “classic influencing factors”, “influencing factors changed by remote working”, and “new remote working influencing factors”. Based on the influencing factors on job satisfaction, a survey has been conducted with n = 684 employees in the service sector. Cronbach’s alpha of the individual constructs was shown to be suitable. Furthermore, the construct validity of the constructs was confirmed by face validity, content validity, convergent validity (AVE > 0.5: CR > 0.7), and discriminant validity. Additionally, confirmatory factor analysis (CFA) confirmed the model fit for the investigated sample (CMIN/DF: 2.567; CFI: 0.927; RMSEA: 0.048). The SEM-analysis has shown that the most significant influencing factor on job satisfaction is “identification with the work” with β = 0.540, followed by “Appreciation” (β = 0.151), “Compensation” (β = 0.124), “Work-Life-Balance” (β = 0.116), and “Communication and Exchange of Information” (β = 0.105). While the significance of each factor can vary depending on the work model, the SEM-analysis shows that the identification with the work is the most significant factor in all three work models and, in the case of the traditional office work model, it is the only significant influencing factor. The study shows that employees who work entirely remotely or have a hybrid work model are significantly more satisfied with their job, with a job satisfaction score of 5.0 respectively on a scale from 1 (very dissatisfied) to 7 (very satisfied), than employees do not have the option to work from home with a score of 4.6. This comes as a result of the lower identification with the work in the model without any remote working. Furthermore, the responses indicate that it is important to consider the individual preferences of each employee when it comes to the work model to achieve overall higher job satisfaction. Thus, it can be argued that companies can profit off of more motivation and higher productivity by considering the individual work model preferences, therefore, increasing the identification with the respective work.

Keywords: home-office, identification with work, job satisfaction, new work, remote work, structural equation modeling

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Authors: Georgios A. Fragkiadakis, Antonia Psaroudaki, Theodora Mouratidou, Eirini Sfakianaki

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Research: The nutritional benefits of eating carob are many and important for the human organism, as it is a food rich in carbohydrates and low in fat and contains multiple nutrients, making it a "superfood". Within the framework of the project "Actions for the optimal utilization of the potential of carob in the Region of Crete" which is financed-supervised by the Region of Crete, a second-grade local self-government authority, with the collaboration of the University of Crete and of the Hellenic Mediterranean University, an online survey was carried out with the aim of evaluating dietary habits and views related to the consumption of carob and its products in a sample of local residents. Results and Conclusions: Of the 351 participants, 259 (73.8%) stated that they consume carob products, and 26.2% stated that they do not. Difficult access and limited availability of carob-food products (33.7%), high price (20.7%), and difficulties of use and preparation (15.2%) were cited as the main reasons for non-consumption. Other reasons, to a lesser extent, concern the taste, especially the sweet aftertaste of some products. Concerning the behavior and eating habits related to the consumption of carob products (n=259), 57.9% of the sample report that they buy carob products "sometimes"; 21.2% report "often"; 19.7% report "rarely", and a very small percentage of 1.2% report "constantly". With reference to the reasons for choosing carob products, the participants mention the main reason for their high nutritional value (51.7%), followed by 32.4% of nutritional claims and health claims, and the organoleptic characteristics (10.8%). Other positive factors are the final price of the product, the ease of use, and the respect for the local environment and producers. Some bakery products show the highest percentage of consumption among carob-food consumers, mainly in the form of rusks (86.1%) and breadsticks (70.3%). They are followed, in descending order, by bread (63.3%), toast (52.1%), and flour (50.6%). More specifically: 40.5% consume carob rusks less than once a month; 22% consume less than once a week; up to twice a week 12.4%; 6.6%, consume rusks 3 to 4 times a week, and daily 3.9%. It is worth mentioning that a high percentage of consumers of carob products recommend the consumption to their family and friends. Only a small percentage, in the range of 5%, does not recommend the consumption of carob products in their close family/social circle. The main motivating factors for the consumption of carob products are the expected effects they may have on health (74.1%) and the organoleptic characteristics with a percentage of 21.6%.

Keywords: food, consumer, preferences, carob, Crete, Greece

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Authors: Silvia Rocha, Flavio Tiago, Maria Teresa Tiago, Sandra Faria, Joao Couto

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The Azores have a tourist potential that has been developing, especially after an increase in promotion and the liberalization of airspace. However, there is still a gap with regard to the understanding of tourists from North America. Previous studies referred to the existence of two basic types of touristic flows: Emigrants and locals. Looking to help fill this gap, a study of travelers from North America was conducted. Using cluster analysis, it was determined the existence of three segments: nostalgic, regular and frequent. The recognition of these three segments is important to determine the necessary adjustments in tourist offerings to this market.

Keywords: tourism, diaspora, nostalgia, culture

Procedia PDF Downloads 193

Authors: Shan Wang, Fahad Alhathal, Hari Subramanian

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User-centered design skills play an important role in crafting a positive and intuitive user experience for software applications. Embracing a user-centric design approach involves understanding the needs, preferences, and behaviors of the end-users throughout the design process. This mindset not only enhances the usability of the software but also fosters a deeper connection between the digital product and its users. This paper encompasses a 6-month knowledge exchange collaboration project between an academic institution and an external industry in 2023 in the UK; it aims to improve the user experience of a digital platform utilized for a knowledge management tool, to understand users' preferences for features, identify sources of frustration, and pinpoint areas for enhancement. This research conducted one of the most effective methods to implement user-centered design through co-design workshops for testing user onboarding experiences that involve the active participation of users in the design process. More specifically, in January 2023, we organized eight co-design workshops with a diverse group of 11 individuals. Throughout these co-design workshops, we accumulated a total of 11 hours of qualitative data in both video and audio formats. Subsequently, we conducted an analysis of user journeys, identifying common issues and potential areas for improvement within three insights. This analysis was pivotal in guiding the knowledge management software in prioritizing feature enhancements and design improvements. Employing a user-centered design thinking process, we developed a series of graphic design solutions in collaboration with the software management tool company. These solutions were targeted at refining onboarding user experiences, workplace interfaces, and interactive design. Some of these design solutions were translated into tangible interfaces for the knowledge management tool. By actively involving users in the design process and valuing their input, developers can create products that are not only functional but also resonate with the end-users, ultimately leading to greater success in the competitive software landscape. In conclusion, this paper not only contributes insights into designing onboarding user experiences for software within a co-design approach but also presents key theories on leveraging the user-centered design process in software design to enhance overall user experiences.

Keywords: user experiences design, user centered design, co-design approach, knowledge management tool

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: G. T. Aigarinova

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This article considers the legal issues of food security as a major component of national security of the republic. The problem of food security is the top priority of the economic policy strategy of any state, the effectiveness of this solution influences social, political, and ethnic stability in society. Food security and nutrition is everyone’s business. Food security exists when all people, at all times, have physical, social and economic access to sufficient safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life. By analyzing the existing legislation in the area of food security, the author identifies weaknesses and gaps, suggesting ways to improve it.

Keywords: food security, national security, agriculture, public resources, economic security

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Authors: Mahdi Rahimi, Mohammad Mahdi Mojahedian, Mohammad Reza Aref

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In this paper, a cellular communication scenario with a transmitter and an authorized user is considered to analyze its secrecy in the face of eavesdroppers and the interferences propagated unintentionally through the communication network. It is also assumed that some D2D pairs and eavesdroppers are randomly located in the cell. Assuming hardware impairment, perfect connection probability is analytically calculated, and upper bound is provided for the secrecy outage probability. In addition, a method based on random activation of D2Ds is proposed to improve network security. Finally, the analytical results are verified by simulations.

Keywords: physical layer security, stochastic geometry, device-to-device, hardware impairment

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Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

Procedia PDF Downloads 352

Authors: J. George, M. Al Mutairi, W. Aljuryyad, A. Alhussanan, A. Alkashan, T. Aldoghiri, Z. Alamari, A. Albakr

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Introduction: Students make plans for their career and are keen in exploring options of employment in those carriers. They make their employment choice based on their desires and preferences. This study aims to identify if students of King Saud Bin Abdulaziz for Health Sciences, College of Applied Medical Sciences after obtaining appropriate education prefer to work as clinicians, university faculty, or full-time researchers. There are limited studies in Saudi Arabia exploring the university student’s employment choices and preferences. This study would help employers to build the required job positions and prevent misleading employers from opening undesired positions in the job market. Methodology: The study included 394 students from third and fourth years both male and female among the eighth programs of college of applied medical sciences, King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh campus. A prospective quantitative cross-sectional study was conducted; data were collected by distributing a seven item questionnaire and analyzed using SPSS. Results: Among the participants, 358 (90.9%) of them chose one of the three listed career choices, 263 (66.8%) decided to work as hospital staff after their education, 75 students (19.0%) chose to work as a faculty member in a university after obtaining appropriate degree, 20 students (5.1%) preferred to work as full-time researcher after obtaining appropriate degree, the remaining 36 students (9.1%) had different career goals, such as obtaining a master degree after graduating, to obtain a bachelor of medicine and bachelor in surgery degree, and working in the private sector. The most recurrent reason behind the participants' choice was "career goal", where 276 (70.1%) chose it as a reason. Conclusion: The findings of the study showed that most student’s preferred to work in hospitals as clinicians, followed by choice of working as a faculty in a university, the least choice was to be working as full-time researchers.

Keywords: College of Applied Medical Sciences, employment ambitions, graduating students, King Saud bin Abdulaziz University for Health Sciences

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Authors: Felix Golla

Abstract:

In an era where technology increasingly intersects with education, the potential of chatbots and ChatGPT agents in enhancing student learning experiences in higher education is both significant and timely. This study explores the integration of these AI-driven tools in educational settings, emphasizing their design and functionality to meet the specific needs of students. Recognizing the gap in literature concerning student-centered AI applications in education, this research offers valuable insights into the role and efficacy of chatbots and ChatGPT agents as educational tools. Employing qualitative research methodologies, the study involved conducting semi-structured interviews with university students. These interviews were designed to gather in-depth insights into the students' experiences and expectations regarding the use of AI in learning environments. The High-Performance Cycle Model, renowned for its focus on goal setting and motivation, served as the theoretical framework guiding the analysis. This model helped in systematically categorizing and interpreting the data, revealing the nuanced perceptions and preferences of students regarding AI tools in education. The major findings of the study indicate a strong preference among students for chatbots and ChatGPT agents that offer personalized interaction, adaptive learning support, and regular, constructive feedback. These features were deemed essential for enhancing student engagement, motivation, and overall learning outcomes. Furthermore, the study revealed that students perceive these AI tools not just as passive sources of information but as active facilitators in the learning process, capable of adapting to individual learning styles and needs. In conclusion, this study underscores the transformative potential of chatbots and ChatGPT agents in higher education. It highlights the need for these AI tools to be designed with a student-centered approach, ensuring their alignment with educational objectives and student preferences. The findings contribute to the evolving discourse on AI in education, suggesting a paradigm shift towards more interactive, responsive, and personalized learning experiences. This research not only informs educators and technologists about the desirable features of educational chatbots but also opens avenues for future studies to explore the long-term impact of AI integration in academic curricula.

Keywords: chatbot design in education, high-performance cycle model application, qualitative research in AI, student-centered learning technologies

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Authors: Zihuan (Dylan) Wang

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This paper analyzes the formation of human food aversion from a biological perspective. It points out that this biased behavior is formed through the accumulation of long-term survival and life experiences. By introducing the "Food Chain Energy Pyramid" model and the analogous deduction of the "Human Food Aversion Pyramid," with energy conversion efficiency as the primary reason, it analyzes the underlying reasons for the formation of food preferences. Food industry professionals can gain inspiration from this article to combine the theory presented with their expertise in order to leverage product quality and promote environmentally conscious practices.

Keywords: food aversion, food preference, energy conversion efficiency, food and culture, nutrition, research and development

Procedia PDF Downloads 59

Authors: Sven Tackenberg, Sönke Duckwitz, Andreas Petz, Christopher M. Schlick

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This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem (RCPSP) to apply it to complex maintenance problems. The problem is to assign technicians to a team which has to process several tasks with multi-level skill requirements during a work shift. Here, several alternative activities for a task allow both, the temporal shift of activities or the reallocation of technicians and tools. As a result, switches from one valid work process variant to another can be considered and may be selected by the developed evolutionary algorithm based on the present skill level of technicians or the available tools. An additional complication of the observed scheduling problem is that the locations of the construction sites are only temporarily accessible during a day. Due to intensive rail traffic, the available time slots for maintenance and repair works are extremely short and are often distributed throughout the day. To identify efficient working periods, a first concept of a Bayesian network is introduced and is integrated into the extended RCPSP with pre-emptive and non-pre-emptive tasks. Thereby, the Bayesian network is used to calculate the probability of a maintenance task to be processed during a specific period of the shift. Focusing on the domain of maintenance of the railway infrastructure in metropolitan areas as the most unproductive implementation process at construction site, the paper illustrates how the extended RCPSP can be applied for maintenance planning support. A multi-criteria evolutionary algorithm with a problem representation is introduced which is capable of revising technician-task allocations, whereas the duration of the task may be stochastic. The approach uses a novel activity list representation to ensure easily describable and modifiable elements which can be converted into detailed shift schedules. Thereby, the main objective is to develop a shift plan which maximizes the utilization of each technician due to a minimization of the waiting times caused by rail traffic. The results of the already implemented core algorithm illustrate a fast convergence towards an optimal team composition for a shift, an efficient sequence of tasks and a high probability of the subsequent implementation due to the stochastic durations of the tasks. In the paper, the algorithm for the extended RCPSP is analyzed in experimental evaluation using real-world example problems with various size, resource complexity, tightness and so forth.

Keywords: maintenance management, scheduling, resource constrained project scheduling problem, genetic algorithms

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Authors: Funda Kul, İsmail Gür

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Pension scheme providers have to price mortality risk by accurate mortality forecasting method. There are many mortality-forecasting methods constructed and used in literature. The Lee-Carter model is the first model to consider stochastic improvement trends in life expectancy. It is still precisely used. Mortality forecasting is done by mortality index in the Lee-Carter model. It is assumed that mortality index fits ARIMA time series model. In this paper, we propose and use dynamic normal inverse gaussian distribution to modeling mortality indes in the Lee-Carter model. Using population mortality data for Italy, France, and Turkey, the model is forecasting capability is investigated, and a comparative analysis with other models is ensured by some well-known benchmarking criterions.

Keywords: mortality, forecasting, lee-carter model, normal inverse gaussian distribution

Procedia PDF Downloads 360